Multi-platform data processing system

ABSTRACT

Systems, methods, apparatus, computer program code and means to improve dynamic data processing associated with one or more automated rating applications are provided. In some embodiments, an apparatus may include a communication device to receive a business type and a business location. The apparatus may also include a business owner&#39;s policy coverage or standalone general liability coverage platform to query a rating database, the query resulting in at least a first price for an entity. The apparatus may further include a professional liability coverage platform operating in parallel with a business owner&#39;s platform.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation application of co-pending U.S. patentapplication Ser. No. 16/871,283 entitled “System For Multi-Platform DataProcessing” and filed on May 11, 2020, which is in turn a continuationof U.S. patent application Ser. No. 14/876,252 entitled “System ForImproved Network Data Processing” and filed on Oct. 6, 2015, now U.S.Pat. No. 10,650,461, the entire disclosures of all of which areincorporated by reference herein for all purposes.

BACKGROUND

Data driven businesses increasingly are dependent on a wider variety andamount of data derived or accessed from multiple disparate sources andnetworks. Significant challenges exist with integrating complex andsophisticated information technology solutions in order to performlarge-scale transactional data management while maintaining data qualityand access. Advanced intelligence and analytics are needed to helpbusinesses understand and make use of complex data for effective andreliable decision making.

Small and medium size businesses span a wide range of business types,and involve a wide range of business risks and risk characteristics,making it difficult to generate and analyze information to producerating and pricing policies that can be reliably and consistentlyapplied to different businesses in different geographical locations.

The rating and pricing of business owner's policies is complex, and ismade particularly complex by the wide range of different types andclasses of businesses. Rating and pricing is made even more complex bythe different geographical, demographic and even environmentalconditions that are relevant to the risk of loss for differentbusinesses. For example, certain areas of the U.S. present higher lossrisks due to catastrophic conditions such as hurricanes or floods. Asanother example, certain areas present higher loss risks due to theft.Current rating and pricing systems do not adequately take suchterritorial variations into consideration when pricing and evaluatingbusiness owner's policies.

In addition to business owner's policy coverage, certain types ofbusinesses may be interested in professional liability insurance. Forexample, a professional public speaker might be interest in obtaininginsurance against the risk being accused of making slanderous orlibelous statements. Similarly, an accounting or auditing service mightbe interested in obtaining insurance again the risk of being accused ofmisconduct. Different types of businesses, however, can be associatedwith different types of professional liability risks, and determining anappropriate premium value for such insurance can be a time consumingtask (especially when underwriting determinations are not begun untilafter the business owner's policy coverage is calculated).

It would be desirable to provide systems and methods for rating andpricing insurance policies which achieve faster, better rate and pricingspecificity and flexibility. It would further be desirable to provideestablished base rates by coverage, amount of insurance relativities,territories or geographical location. It would be further desirable toprovide systems and methods that allow existing agent systems andprocesses to quickly and efficiently price and quote business ownerinsurance policies, including professional liability coverage, using theimproved rating and pricing systems.

SUMMARY OF THE INVENTION

According to some embodiments, systems, methods, apparatus, computerprogram code and means for rating and pricing insurance policies areprovided. In some embodiments, to improve processing associated with adynamic automated rating application may include a communication deviceto receive, from a remote device via a communication network,information associated with a business to be insured including a quoterequest for the business specifying a business type and a businesslocation. The apparatus may also include a business owner's policycoverage or standalone general liability coverage platform coupled tothe communication device, including a first processor coupled to thecommunication device and a first storage device in communication withthe first processor and storing instructions adapted to be executed bythe processor to: (i) identify at least a first applicable businessowner's policy coverage or standalone general liability coverage, (ii)based on the at least a first applicable business owner's policycoverage or standalone general liability coverage, identify at least afirst relevant coverage formula including at least a first territoryfactor, the at least a first territory factor based on a geographicallocation of the business, and (iii) query a rating database using the atleast a first relevant coverage formula, the business type and thebusiness location, the query resulting in at least a first price for theat least first applicable business owner's policy coverage. Theapparatus may further include a professional liability coverage platformcoupled to the communication device, operating in parallel with thebusiness owner's policy coverage platform and including a secondprocessor and a second storage device in communication with the secondprocessor and storing instructions adapted to be executed by theprocessor to: (i) based on the business type, modify a base professionalliability form with at least one product feature, and (ii) query aprofessional liability rating database using the business type, businesslocation, and the at least one product feature, the query resulting in aprofessional liability premium value, wherein the apparatus is totransmit a response to the quote request, the response including the atleast a first price, the at least first applicable coverage, and theprofessional liability premium value.

According to some embodiments, a quoting process using the rating sheetsgenerated pursuant to some embodiments includes receiving a quoterequest associated with a business, the quote request specifying abusiness type and a business location, identifying at least firstapplicable coverage, based on said at least first applicable coverage,identifying at least a first relevant coverage formula, querying arating database using said at least first relevant coverage formula,said business type and said business location, said query resulting inat least a first price for said at least first applicable coverage, andtransmitting a response to said quote request, said response includingsaid at least first price and said at least first applicable coverage.

Other embodiments include: means for receiving, from a remote device viaa communication network, information associated with a business to beinsured including a quote request for the business specifying a businesstype and a business location; means for identifying at least a firstapplicable business owner's policy coverage or standalone generalliability coverage; based on the at least a first applicable businessowner's policy coverage, means for identifying at least a first relevantcoverage formula including at least a first territory factor, the atleast a first territory factor based on a geographical location of thebusiness; means for querying a rating database using the at least afirst relevant coverage formula, the business type and the businesslocation, the query resulting in at least a first price for the at leastfirst applicable business owner's policy coverage; based on the businesstype, means for modifying a base professional liability form with atleast one product feature; means for querying a professional liabilityrating database using the business type, business location, and the atleast one product feature, the query resulting in a professionalliability premium value means for transmitting a response to the quoterequest, the response including the at least a first price, the at leastfirst applicable coverage, and the professional liability premium value.

In some embodiments, a communication device associated with an automatedinsurance processing platform exchanges information with remote devices.The information may be exchanged, for example, via public and/orproprietary communication networks.

A technical effect of some embodiments of the invention is an improvedand computerized insurance rating and quoting system providing faster,better rate and pricing specificity and flexibility for businessinsurance policies. With these and other advantages and features thatwill become hereinafter apparent, a more complete understanding of thenature of the invention can be obtained by referring to the followingdetailed description and to the drawings appended hereto.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is block diagram of a system according to some embodiments of thepresent invention.

FIG. 2 illustrates a method according to some embodiments of the presentinvention.

FIGS. 3A and 3B illustrate a further method according to someembodiments of the present invention.

FIG. 4 illustrates a further method according to some embodiments of thepresent invention.

FIG. 5 illustrates a further method according to some embodiments of thepresent invention.

FIG. 6 illustrates a further method according to some embodiments of thepresent invention.

FIG. 7 is a block diagram of an insurance apparatus in accordance withsome embodiments of the present invention.

FIGS. 8A and 8B illustrate graphical depictions of portions of territorymaps in accordance with some embodiments of the present invention.

FIG. 9 illustrates an input data table used in selecting territorygroupings according to some embodiments of the present invention.

FIG. 10 illustrates a chart for analyzing territory groupings inaccordance with some embodiments of the present invention.

FIG. 11 is a high level overview of insurance processes associated withvarious categories of underwriting risks according to some embodiments.

FIG. 12 illustrates a graphical user interface to provide professionalliability rating information according to some embodiments.

FIG. 13 is a portion of a tabular database storing insurance informationin accordance with some embodiments.

FIG. 14 illustrates a tablet computer displaying insurance relatedinformation according to some embodiments.

DETAILED DESCRIPTION

The present invention provides significant technical improvements tofacilitate dynamic data processing. The present invention is directed tomore than merely a computer implementation of a routine or conventionalactivity previously known in the industry as it significantly advancesthe technical efficiency, access and/or accuracy of communicationsbetween devices by implementing a specific new method and system asdefined herein. The present invention is a specific advancement in theareas of parallel rating applications by providing technical benefits indata accuracy, data availability and data integrity and such advancesare not merely a longstanding commercial practice. The present inventionprovides improvement beyond a mere generic computer implementation as itinvolves the processing and conversion of significant amounts of data ina new beneficial manner as well as the interaction of a variety ofspecialized insurance, client, and/or third party systems, networks andsubsystems. For example, in the present invention information may betransmitted from remote devices to the rating applications and may thenbe analyzed accurately, in parallel to improve response times, andautomatically collected by an enterprise.

FIG. 1 is block diagram of an insurance system 100 according to someembodiments of the present invention. The system 100 may, for example,facilitate the creation of rating schedules for business insurancepolicies as well as perform the quoting, rating and pricing ofindividual policies, including “professional liability insurance,” usingthose rating schedules. As used herein, the phrase “professionalliability insurance,” also known as error and omissions insurance, mayrefer to liability insurance that helps protect entities from payingdefense and/or indemnity costs relating to a claim made by a clientalleging an actual or alleged negligent act, error or omission in theperformance of or failure to perform professional services. Note thatprofessional liability insurance may be associated with MiscellaneousProfessional Liability (“MPL”) insurance and/or Accountant ProfessionalLiability (“APL”) insurance.

According to some embodiments, a business owner's policy coverage“automated” insurance processing platform 110 may be provided. As usedherein the term “automated” indicates that at least some part of a stepassociated with a process or service is performed with little or nohuman intervention. By way of examples only, the platform 110 may beassociated and/or communicate with a Personal Computer (PC), anenterprise server, a database farm, and/or a consumer device. Thebusiness owner's policy coverage automated insurance processing platform110 may, according to some embodiments, apply rating schedules, andprice and rate individual business policies using those ratingschedules. Pursuant to some embodiments, the rating schedules includeone or more territory factors used to incorporate geographical,demographic and territorial loss data into the rating and pricing ofpolicies.

As shown, the business owner's policy coverage automated insuranceprocessing platform 110 may include a number of modules or components,including one or more analysis modules 112, quoting modules 114 andissuing modules 116. As will be described further below, the analysismodules 112 may be used in conjunction with the creation and updating ofone or more rating schedules for use in pricing and rating businessinsurance policies pursuant to embodiments of the present invention. Forexample, in some embodiments, the analysis modules 112 are used toanalyze historical loss data in conjunction with geographic anddemographic data to generate territory factors for use in rating andpricing business insurance policies. In some embodiments, as will bedescribed further below, the territory factors are used in conjunctionwith one or more rating formulas to allow improved pricing and analysisof business insurance policies. The analysis modules 112 may includecode or other modules used to analyze and generate expense fees, analyzeand create rating factors, analyze and create territory factors, andperform pricing analysis. The analysis modules 112 may be operated tocreate data for storage and use in one or more rating databases 120. Thedata stored in rating databases 120 are accessed by the business owner'spolicy coverage automated processing platform 110 to allow quoting andissuing of policies using the ratings data.

A professional liability coverage automated insurance processingplatform 150 may include a number of modules or components, includingone or more analysis modules, quoting modules 154 and issuing modules156. As will be described further below, the analysis modules 112 may beused in conjunction with the creation and updating of one or more ratingschedules for use in pricing and rating professional liability insurancepolicies pursuant to embodiments of the present invention. For example,in some embodiments, quoting module 154 is used to modify a baseprofessional liability form with at least one product feature (e.g., adeductible amount, coverage limit, etc.). Note that a base professionalliability form may, according to some embodiments, be modified by onemandatory class-specific endorsement. In some embodiments, as will bedescribed further below, the professional liability coverage automatedinsurance processing platform 150 operates in parallel with the businessowner's policy coverage automated processing platform 110 to determine aprofessional liability premium value for a quote. The quoting module 154may include code or other modules used to analyze and generate expensefees, analyze and create rating factors, analyze and create territoryfactors, and perform pricing analysis. The analysis modules may beoperated to create data for storage and use in one or more ratingdatabases 120. The data stored in rating databases 120 may be accessedby the professional liability automated processing platform 150 to allowquoting and issuing of policies using the ratings data.

The quoting modules 114, 154 may be used in conjunction with thequoting, rating and pricing of individual business insurance policies(e.g., in response to requests for quotes received from agents operatingagent devices 130 or business devices 132).

The business owner's policy coverage automated insurance processingplatform 110 and the analysis modules 112 may access information in oneor more databases 117-118. The databases may include, for example, riskcharacteristic data 117 and historical loss data 118 associated withpreviously-issued insurance policies. As will be described furtherbelow, the risk characteristic data 117 and the historical loss data 118may be used by the analysis modules 112 in the creation and updating ofrating schedules for the storage in one or more rating databases 120 foruse by the business owner's policy coverage processing platform 110 inquoting, pricing and issuing new business insurance policies.

The business owner's policy coverage automated insurance processingplatform 110 and the analysis modules 112 may also have access to datafrom one or more external data sources 140. The external data sources140 may include data used, for example, by the analysis modules 112 inthe analysis and generation of rating tables. For example, external datasources 140 providing demographic, geographic, and climate data may beaccessed by the business owner's policy coverage automated insuranceprocessing platform 110. In some embodiments, the external data sources140 may include publicly accessible data (e.g., such as U.S. ZIP codedata and U.S. census data).

The business owner's policy coverage automated insurance processingplatform 110 might access the databases 117-120 and the external datasources 140 via one or more communication networks. These devices (andany of the other devices described herein) could be associated with, forexample, a server, a PC, a mobile or laptop computer, or any otherappropriate storage and/or communication device to exchange informationvia a web site and/or a communication network. As used herein, devices(including those associated with the business owner's policy coverageautomated insurance processing platform 110, the databases 117-120, theexternal data sources 140, the agent devices 130, business devices 132,and any other device described herein) may exchange information via anycommunication network, such as a Local Area Network (LAN), aMetropolitan Area Network (MAN), a Wide Area Network (WAN), aproprietary network, a Public Switched Telephone Network (PSTN), aWireless Application Protocol (WAP) network, a Bluetooth network, awireless LAN network, and/or an Internet Protocol (IP) network such asthe Internet, an intranet, or an extranet. Note that any devicesdescribed herein may communicate via one or more such communicationnetworks. According to some embodiments, one or more business devicesmay be associated with a claims processing system adapted to receiveinformation about a claim, determine Extended Reporting Period (“ERP”)information associated with the claim, and provide claim data to anautomated dynamic rating application.

The business owner's policy coverage automated insurance processingplatform 110 and/or databases 117-120 may be, according to someembodiments, accessible via a Graphical User Interface (GUI). The GUImight be used, for example, to dynamically display existing insurancepolicy information, analyze historical or demographic data to generateterritory factors for use in rating formulas, generate updated or newrating tables, receive requests for business insurance policyinformation, and/or to associate one or more cost of insurance rateswith an existing or proposed policy.

Although single automated insurance processing platforms 110, 150 areshown in FIG. 1, any number of such devices may be included. Moreover,various devices described herein might be combined according toembodiments of the present invention. For example, in some embodiments,the business owner's policy coverage automated insurance processingplatform 110 and databases 117-120 might be co-located and/or maycomprise a single apparatus. In some embodiments, the analysis modules112, and the analysis and generation of ratings factors (such as theterritory rating factors described below) is performed using one or moreseparate systems. In some embodiments, some or all of the analysis maybe performed using a spreadsheet or other analytic program.

FIG. 2 illustrates a method that might be performed, for example, bysome or all of the elements of the system 100 described with respect toFIG. 1 according to some embodiments. The flow charts described hereindo not imply a fixed order to the steps, and embodiments of the presentinvention may be practiced in any order that is practicable. Note thatany of the methods described herein may be performed by hardware,software, or any combination of these approaches. For example, acomputer-readable storage medium may store thereon instructions thatwhen executed by a machine result in performance according to any of theembodiments described herein.

The process 200 may be performed to generate (or update) a ratingsdatabase to allow the quoting, pricing and issuance of businessinsurance policies using features of the present invention. Pursuant tosome embodiments, process 200 involves processing at 202 where ratingsfactors (including territory factors) and base premium amounts arecalculated and generated, processing at 204 where the ratings databaseis updated with those factors and premiums, and processing at 206 wherea set of formulas is created and stored for use in accessing the ratingsdata for use in the pricing of business insurance policies. In someembodiments, process 200 may be used to create ratings data for eachregulated territory or region. As a specific example, where the process200 is used to create ratings data for a State in the United States,process 200 is performed on a State-by-State basis to produce ratingsdata and formulas for the issuance and quoting of policies in a singleState. As will be discussed further below, the individual ZIP codeswithin a State are analyzed to create territory factors for ratinginsurance policies within the State.

According to some embodiments, the creation of a ratings databaseinvolves the generation of a number of rating factors and premiums. Forexample, to allow the calculation of a property liability insurancepremium for a business, the following rating factors may be generatedand stored in the ratings database: market group factors, class factors,amount of insurance factors, protection class factors, constructionclass factors, building age factors, number of locations factors, etc.Some of these factors may be created using traditional techniques usedin the art. Pursuant to some embodiments, additional factors, referredto herein as territory factors, are provided which are created usingtechniques of the present invention. These territory factors allow moreaccurate and predictive pricing of business insurance premiums, and maybe created using the process described below in conjunction with FIG. 3.

Process 200 results in the generation of ratings data (or tables) whichmay be accessed or used in the pricing of business insuranceapplications using a set of formulas selected based on a policy type.For example, an application for a property liability insurance policymay result in the execution of a building rating formula which retrievesdata from ratings database. An example of a portion of a building ratingformula is: Calculated Premium=[Base Premium]*[Market GroupFactor]*[Class Factor]*[Territory Factor].

In the sample building rating formula shown above, each of the variables(contained in brackets) are retrieved from the ratings database (such asthe database 120 of FIG. 1) based on information stored in the ratingsdatabase in process 200. Those skilled in the art, upon reading thisdisclosure, will appreciate that a number of different types of ratingformulas may be used. For example, the system 100 may use some or all ofthe following rating formulas to access ratings data from ratingsdatabase 120 to price business insurance policies: a building ratingformula, a business personal property rating formula, a personalproperty of others rating formula, products rating formulas, PREM/OPSrating formulas, business interruption ratings formulas, an equipmentbreakdown rating formula, a policy expense fee rating formula, and oneor more optional coverage rating formulas.

Some or all of the steps of process 200 may be repeated as needed (e.g.,on a regular or scheduled basis) to ensure that the ratings data isup-to-date and accurately reflects risk and pricing conditions, and toensure that appropriate and relevant formulas are used in the operationof the system.

Applicants have recognized that territory or geographical conditions maybe used to improve the pricing and analysis of business insurancepolicies. The generation, smoothing and use of territory factorspursuant to some embodiments will now be described by reference to FIG.3. Pursuant to some embodiments, a set of territories (or geographicalregions) are created which are assigned a territory rating factor foruse in the ratings database 120 of FIG. 1. In some embodiments, the setof territories is based on ZIP codes within a State being analyzed. Aswill be described further below, groups or sets of ZIP codes havingsimilar characteristics are grouped together and assigned similarterritory factors. The number of ZIP codes within a grouping or set isselected to improve the predictive power and to reduce statisticalnoise, as well as to increase the perceived or actual fairness ofratings within a geographical region. Pursuant to some embodiments, theterritorial data used in generating the territory factors is generatedusing an analysis of geographic (ZIP code), demographic, and other data.Pursuant to some embodiments, a set of rating boundaries is created byState (or other geographic region). In one embodiment, the ratingboundaries are based on ZIP code regions and encompass contiguousgeographic areas. Those skilled the art will appreciate that otherboundary regions may also be used.

In some embodiments, a result of the territory analysis is a set ofterritory factors (for use in rating formulas, and stored in a ratingdatabase or table) with a set of territories having territoryboundaries. In some embodiments, the same territory boundaries willapply to all classes of businesses (e.g., the same territory will beused to identify the rating information for a manufacturing business aswell as a retail business). Pursuant to some embodiments, historicalloss data is analyzed (by ZIP code, for example) to determine a set ofboundaries having reliably grouped ZIP codes with the most similar riskof loss within a territory. The analysis, in some embodiments, attemptsto identify the greatest differences between territories (e.g., so thatthe most similar risk of losses are within the same territories).

Pursuant to some embodiments, separate territory factors may begenerated for liability insurance, property insurance, and insurancebased on catastrophe-related losses (such as floods, hurricanes, etc.).In such embodiments, separate ratings tables, with separate territoryfactors, may be created and stored in ratings database 120 for access bythe quoting module 114 in the quoting and pricing of new policies. Theterritory factors may be generated using a process such as the processdescribed below in conjunction with FIGS. 3A and 3B.

The processing of FIG. 3A begins at 302 where the automated processingplatform 102 receives (or otherwise obtains) historical loss data (e.g.,from database 118), geographic data (e.g., from external data source140) and demographic data (e.g., from external data source 140) foranalysis. Data received at 302, in some embodiments, also includesoutputs from predictive models (e.g., such as generalized linear models,or “GLMS”) which are run to predict the statistical likelihood of futureclaims or losses based on past data (such as the historical loss data).GLMS may be used to predict claim frequency, claim severity or purepremium. The historical loss data may be segmented into both frequencyof loss and severity of loss data. The frequency and severity data, insome embodiments, may be obtained from historical loss data and may besupplemented using external data.

For example, the following external demographic, geographic, and climatedata may be used to supplement historical property coverage information:population and population density data, percentage of populationmarried, percentage of population with college education, percentage ofpopulation using public transportation, percentage of buildings builtbefore a certain date (e.g., such as 1960), crime rate, weather data,including rainfall, snowfall, and temperature data. This externaldemographic, geographic and climate data may be obtained, for example,from a number of publicly available data sources. Pursuant to someembodiments, the territory data is based on U.S. ZIP code data (althoughdata from other sources or regions may also be used).

The outputs from the predictive models may further include dataidentifying one or more significant geo variables which are identifiedto reduce the geographical or demographic influence on claim or lossdata. For example, there are more thefts where the crime rate is highand more slip and fall claims occur where population density is highestand/or sidewalks are icy. Other significant geo variables may includethe percentage of businesses in a ZIP code making a claim, the crimerate in a ZIP code, or the like. The significant geo variables may bebased on ZIP codes within a territory or region being analyzed (e.g.,such as a State). The GLMS may also be operated to perform an analysisof the frequency and severity of historical loss data by coverage type(e.g., where “coverage type” may include property coverage or “PREM/OPS”coverage), by peril of loss for property coverage (e.g., where perilsinclude theft, fire, water, non-hurricane wind, hurricane wind, andother), and by type of exposure base for PREM/OPS coverage (such assales numbers, payroll size, and property square footage).

Processing continues at 304 where the loss data received at 302 isadjusted to remove the effects of risk characteristics and thesignificant geo variables. That is, processing at 304 includes removingcertain variables having an impact on the loss data which are associatedwith geographic and other variables.

Processing continues at 306 where the dataset created at 304 isprocessed using a residual smoothing procedure to account for knownvariables and remove items not driven by generic risk characteristics ona geographic basis. This smoothing process is performed by territory(e.g., by State). For example, in some embodiments, processing at 306includes generating exposure data by ZIP code, and then performing aresidual smoothing process on the exposure data to improve the data(e.g., by removing residual or statistical noise) for further analysis.In some embodiments, an algorithm such as the following formula, may beused, although those skilled in the art, upon reading this disclosure,will recognize that other formulas may be used:

$S_{i} = {\sum\limits_{d_{j} \leq z}\frac{U_{j}}{d_{j}^{y}}}$

Where the smoothed Si is the sum of all the unsmoothed U_(j)'s dividedby dj (the distance from ZIP code (i) to ZIP code (j)) with parameters yand z. The formula may be applied for a series of ZIP codes from (i) to(j), where U_(i) is a loss cost or risk characteristic in a ZIP codearea (i). By performing this smoothing on the loss data associated witheach ZIP code, a smoothed data set is produced with noise or otheranomalies removed. The smoothed data set is then combined with thesignificant geo variables (that were removed at 304) at 308.

Processing continues at 310 where an iterative clustering analysis isperformed on the data created at 308. In general, processing at 310involves iteratively grouping or clustering the ZIP code data for theState or region being analyzed to produce groups of ZIP codes that havesimilar loss or exposure characteristics. By grouping ZIP codes in thismanner, a reduced number of potential territories are created whichprovide improved pricing and analysis of business insurance policies.Applicants have recognized that a number of benefits arise from suchgroupings. For example, processing overhead is reduced by having fewerterritories. As another example, confusion in pricing is avoided. As aspecific example, if territories were not grouped pursuant to thepresent invention, situations may arise where a business in one ZIP codearea may enjoy a low premium, while a similar business in a second ZIPcode (which may be geographically very near the first ZIP code) receivesa second premium, different than the first premium. Such pricingdiscontinuities may be difficult to explain and may not accuratelyrepresent the exposure difference.

Pursuant to some embodiments, the creation and selection of territorygroupings is performed in an iterative process as shown in FIG. 3B. Forexample, the geographical, demographic and loss data may be analyzed inmultiple scenarios, with each scenario involving a different grouping ofZIP codes. Each scenario may include a different sized grouping ofterritories, ranging from about 5 territories to fifty or moreterritories. Each scenario is then analyzed to determine the appropriatesized grouping. For example, the most appropriate sized grouping may beone having sufficient predictive power (e.g., which could occur when toofew territory groups are used) and less statistical noise (e.g., whichcould occur when too many territory groups are used). One process forgrouping territories will now be described by reference to FIG. 3B inconjunction with FIGS. 9 and 10.

The process of FIG. 3B begins at 352 with the loading of ZIP code data,exposure data and loss data (e.g., from FIG. 3A, step 308). FIG. 9 is atable 900 showing a part of an input data set that may be used in theiterative clustering analysis and represents some of the input dataloaded at 352. The table 900 includes a number of columns (including aZIP code column, an exposures in ZIP code column, a smoothed dataexposures column, and a set of one or more ZIP code neighbors columns).The data in the ZIP code column may include all ZIP codes for a State orother region that is being analyzed, and may be obtained from one ormore public data sources. The data in the exposures columns may beobtained from historical loss data sources (e.g., such as historicalloss database 118 of FIG. 1), and the smoothed data may be the datagenerated at step 308. The ZIP code neighbors are those ZIP codes thatare geographically next to the ZIP code in the first column of thetable, and may be obtained from public data sources.

The data in the table 900 is analyzed to generate clusters, or groups ofterritories. As an initial matter, a range of ZIP code clusters may beselected for testing and grouping. The range of clusters to be tested isset at step 356 of FIG. 3B. The size of the range may be based on thesize of the State or region being analyzed. For example, a range of 5-40ZIP codes may be used. The analysis then proceeds in two or moreiterations.

Process 350 continues with a first clustering iteration at 358. Thefirst clustering iteration proceeds as follows. First, the input data(from table 900) is loaded into an analysis macro or tool, and a minimumthreshold exposure for each cluster and a minimum number of ZIP codes isselected. For example, a minimum exposure of 2% and a minimum number of10 ZIP codes may be selected. Next, a copy of the data from table 900 ismade, and each ZIP code is marked, flagged, or otherwise designated as a“cluster”. Next, the data is reduced by merging (e.g., one at a time)the two most similar neighboring clusters that are neighbors where atleast one is in a cluster that does not meet at least one of theminimums set above. This continues until all of the clusters meet theminimums set at 354. The first iteration continues by reducing the twomost similar neighboring clusters (one at a time) until the clustercount is within the input range of clusters to be tested. The output ofthis iteration is stored in memory, and the second iteration isperformed. To “reduce” the most similar neighboring clusters, areduction algorithm may be performed which adds two cluster exposureamounts together, weights two cluster average loss costs together usingexposure data, and then combines two distinct cluster neighbors. Theresult of this first iteration are stored as an interim result at 360,and the original data (from 352) is restored for further processing.

Processing continues at 362 where an increment is established (shown as“X” in FIG. 3B). The increment, for example, may be set as “3” (wherethe clusters will be reduced 3 times). Several other processingvariables may also be set at 362. For example, a variable such as “N”may be set to equal the total number of ZIP codes to be processed (or,the total number of ZIP code areas in the State being analyzed). Acounter variable (shown as “n”) may also be set to 1.

Processing continues at 364 where the clusters are reduced in asubsequent iteration to reduce the clusters by the set increment (in afirst iteration, the clusters are reduced n*X times, or 1*3 times in theexample). Each subsequent iteration begins with the original data (e.g.,the data from table 900). In performing the subsequent iteration, theset increment is used (in the example, the increment is set as 3, so thetotal number of ZIP codes in the State is divided by 3). Each of the ZIPcodes in the table 900 is set as a cluster, and n*X clusters should bereduced by merging (one at a time) the two most similar neighboringclusters without regard to the minimums set forth in 354. The data isthen reduced by merging (one at a time) the two most similar neighboringclusters where at least one is in a cluster that does not meet at leastone of the minimums set in 354. This continues until the cluster countis within the input range of clusters to be tested. Once the input rangeis reached, the output of the first iteration (stored at 360) iscompared to the output of the subsequent iteration (at 366) to determinewhich output is more desirable, and the selected output is stored as thecurrently preferred set.

The reduction may continue until n=N (that is, the reduction at 365 maybe repeated N/X times). Each iteration results in the storage of interimresults and the comparison to the previous “best” clustering at 366until the best clustering from the different iterations is obtained. Atthe end of each iteration, the original data is restored for use by thesubsequent iteration. The best clustering may be stored in spreadsheetfor further analysis and approval by an administrator.

Further analysis and approval of the clustering may be performed bydividing an Expected Value of Process Variance (“EVPV”) by a Variance ofHypothetical Mean (“VHM”) as well as the number of ZIP codes in thelargest cluster to determine the number of clusters to use. In general,the EVPV/VHM is larger when variance within a cluster is larger and whenvariance between clusters is larger. This is shown graphically in FIG.10, where a chart 1000 is shown. The chart 1000 represents hypotheticalEVPV/VHM values plotted against a number of clusters. As shown, thereexists a point 1010 at which the value of increasing clustersdiminishes. In some embodiments, the number of clusters to select forterritory factors in this example is the number of clusters at point1010. Selecting more clusters than this would result in unnecessarycomplexity, selecting fewer clusters may result in territory groups withreduced accuracy.

Once a desired “clustering” of territories has been achieved, processingmay continue at step 312 of FIG. 3A, where the territories may beassigned rating factors and loaded into the ratings database 120 for usein rating and pricing policies. By grouping contiguous or geographicallyproximate territories having similar loss characteristics, Applicantsachieve improved pricing and reduced complexity. Illustrative examplesof territory maps are shown in FIG. 8A (which shows smoothed output,e.g., such as the output from step 308 of FIG. 3A) and FIG. 8B (whichshows contiguous or clustered territories, e.g., such as the output fromstep 310 of FIG. 3A).

FIG. 8A is a graphical representation of a territory map for the Stateof Connecticut. A number of sub-territories are shown, corresponding toZIP code areas established by the U.S. Postal Service. As shown, eachZIP code area is shaded with a color representing a loss risk associatedwith historical loss data from that ZIP code area. The map 800 is theresult of the smoothing process described above as step 308 of FIG. 3A.The sub-territories (or ZIP code areas) with similar shading havesimilar loss risks and other characteristics (e.g., such as the ZIP codeareas marked as items 802 and 804), while other sub-territories or ZIPcode areas have different loss risks and characteristics (e.g., such asthe ZIP code area marked as item 806). FIG. 8B is a similar graphicalrepresentation of a territory map for the State of Connecticut. However,in FIG. 8B, the rating zones have been clustered such that contiguousregions are shown. The contiguous regions represent the clusteringdescribed above (in FIG. 3B) and the map 810 represents the output thatmay exist at step 310 of FIG. 3A, such that ZIP code areas havingsimilar loss characteristics are grouped together. For example, the ZIPcode areas identified as items 812 and 814 correspond to the same ZIPcode areas in FIG. 8A (items 802 and 804), but are now shaded the samecolor (indicating that the ZIP code areas 812 and 814 have been assignedthe same territory risk factor). That is, areas 812 and 814, as a resultof the analysis of the present invention, have been identified as havingsimilar loss characteristics, such that insurance policies issued toentities in those ZIP code areas will be priced using the same territoryrisk factor. As shown in FIG. 8B, the numerous ZIP code areas inConnecticut have been reduced to clusters or contiguous zones, whereeach ZIP code area in a cluster is assigned the same territory riskfactor.

Each type of coverage (e.g., property hurricane, property non-hurricane,and PREM/OPS) may have a separate territory map and may have separateterritory rating factors. Pursuant to some embodiments, these territorymaps help to visually depict how the boundaries can make sense comparedto the risk related data that resulted in their development. Forexample, there will be more territories in large cities with highpopulation and crime rates. Unique territories could also exist becauseof different temperature and/or rainfall patterns. By aligning ratingareas with territorial regions, Applicants have discovered thatdemographic and other data may be reliably analyzed (in conjunction withhistorical loss data) to create rating tables that more closely matchthe loss risks associated with businesses in those territories. Theresult is an ability to quickly and accurately generate pricing data forbusiness insurance policies, given a business location. Business ownersenjoy more accurate pricing of policies, with many business ownersenjoying lower rates based on this greater accuracy.

Pursuant to some embodiments, the process of FIG. 3 may include somefurther review and analysis of the territory boundaries. For example, insome embodiments, further review may include human review (e.g., byfield office staff or the like) to determine whether fewer or additionalboundaries might be needed to align with local knowledge or otherconsiderations. Once the further review and analysis is complete, theterritory boundaries and definitions and associated territory ratingfactors may be finalized for production use in rating prospectivebusiness insurance policies. The processing at 312 may include updatingthe rating database to include territory rating factors for eachcoverage type and territory set may be generated. For example, territoryrating factors may be generated for each U.S. State in which thebusiness insurance policies are to be issued. Each territory ratingfactor may follow the territory breakdown for that State, and territoryrating factors may be generated for each coverage type in each territoryfor the State. A typical rating sheet may include, for example, a numberof territories, a number of products, and a number of rating factors.

Pursuant to some embodiments, a series of update and maintenanceroutines may be performed to ensure that the territory definitionsremain accurate. For example, on a regular or as-needed basis, a processmay be performed to determine whether any changes to ZIP codes haveoccurred requiring updates to the territory definitions. As a specificexample, in some situations the U.S. Postal Service may revise (e.g.,add or remove) ZIP codes. A maintenance process may be performed toidentify such changes and to apply the changes to the productionterritory assignment tables. In some embodiments, the productionterritory assignment tables may be updated on an annual basis (or withevery rate review). In some embodiments, some or all of the steps ofprocess 300 may be performed to update or maintain the rating tables.

Once the rating database has been updated with one or more ratingtables, (and rating formulas and rating factors including territoryrating factors have been updated as will be discussed below), theprocessing platform 102 may be accessed by one or more agents operatingagent devices 130 to request quotes on business insurance policies usingthe rating database. Note that business may directly request aninsurance quote through a business device 132 (e.g., via a web portal)in accordance with any of the embodiments described herein. A quotingprocess 400 will now be described by reference to FIG. 4.

The quoting process 400 may be performed using the rating and quotingsystem 100 of FIG. 1 to rate and quote business insurance policies usingfeatures of the present invention. Quoting process 400 begins at 402where a quote request is received by the processing platform 102.Pursuant to some embodiments, quote requests may be received, forexample, from agents operating agent devices 130 (or business devices140). Agent devices 130 may be computers in communication withprocessing platform 102 over a network connection. In some embodiments,agent devices 130 may be computers with a Web browser which connect toprocessing platform 102 via an Internet connection. Agents operatingagent devices 130 may enter a number of data elements specifying thecharacteristics of the business for which they are seeking a businessinsurance policy quote. For example, the agent may enter data specifyingthe business name, address, business type, and other information. Thisinformation is used by the processing platform 102 to generate a quoteusing the rating tables created as described above.

Processing continues at 404 where the processing platform 102 analyzesthe business information from the quote request to identify one or moreapplicable coverage(s) for the business. For example, the applicablecoverage(s) may depend on factors such as the business type, whether thebusiness owns property, or the like. Once the applicable coverage(s) areidentified, processing continues at 406 where one or more relevantformula(s) corresponding to the applicable coverage(s) are identified.As an example, processing at 406 may include identifying one or more ofthe following formulas as relevant: building coverage, business personalproperty, personal property of others, products, pre-ops, businessinterruption, and equipment breakdown. Each of these formulas may bestored at, or otherwise accessible to, the quoting module 114 ofplatform 110.

Once applicable formulas have been identified, processing continues at408 where the formula(s) are used to query the rating database 120 toretrieve the premium ratings for the business. For example, in quoterequest involving a business having a property to be covered, thebuilding coverage formula may be selected at 406, and then applied at408 to retrieve the current rates for a property having the valuespecified in the quote request in the territory specified. Each formulamay have a number of factors, multipliers and other data look upsrequired to retrieve and apply the relevant rate from the ratingdatabase 120. However, since the rating database 120 and rating formulasinclude the application of territory factors based on discretegeographical areas, the relevance of each premium corresponds tightly tothe data in the quote request, allowing business insurance policies tobe quickly and accurately quoted using embodiments of the presentinvention.

According to some embodiments, professional liability insuranceprocessing may be performed in parallel with the operations describedwith respect to 404 through 408. For example, at 424 a base professionalliability form may be modified, in accordance with a business type, withat least one product feature. At 426, a professional liability databasemay be queried using the business type, business location, and theproduct feature. As a result of the query, a professional liabilitypremium may be determined at 428. Because these operations are performedin parallel, the relevant information may be determined in a more timelyfashion. Moreover, because the same input information is used by bothoperations, the chance of errors in the input information may bereduced.

Once all the rating data has been retrieved from the ratings database120 and applied to the relevant formulas, a quote is constructed at 410.Each quote may involve multiple queries to the ratings database as eachquote may involve the application of multiple ratings formulas. In someembodiments, the quote is constructed after all of the ratings formulashave been applied. Once the quote is completed, the quote, including theprofessional liability premium value, is delivered to the agent deviceand/or business device at 412. When delivered to the agent device, theagent may then present the quote package to the business owner forreview and possible binding.

Embodiments of the present invention simplify the process for generatingquotes for business insurance policies by reducing the number of stepsand manual lookups typically required by agents. Further, the quotesprovided are more accurate and correspond to the nature of the loss riskassociated with businesses as the ratings data is constructed fromactual historical loss data and other demographic and territorial data.

Note that the process 400 may be repeated until a quote package has beenconstructed that satisfies the agent and the business owner.

Pursuant to some embodiments, one or more optional coverages (e.g., suchas an accounts receivables policy, which may be optionally added bycertain qualifying businesses) may also be calculated as a part of thequoting process. For example, in some embodiments, an optional coveragenet rating may be calculated. Pursuant to some embodiments such optionalcoverage net rating may be calculated in an automated fashion usingautomated processing platform 102 of FIG. 1. In some embodiments, theoptional coverage net rating may be calculated upon request by an agent,or automatically in response to a quote request (such as the quoterequest received at 402 of FIG. 4). Calculation of optional coverage netrating will now be described by reference to FIG. 5, where a process 500is shown that may be implemented using the automated processing platform102 of FIG. 1.

Process 500 begins at 502 where the base policy is priced (e.g., inconjunction with the process 400 of FIG. 4). Pursuant to someembodiments, pricing and rates for optional coverage are performed as afunction of an insured's underlying base coverage for the policy (e.g.,an optional accounts receivables coverage is a function of theunderlying contents coverage rating). That is, pursuant to someembodiments, the optional coverage is priced as a direct function of thepricing of the underlying policy. In this manner, the rating variablesare considered in pricing the optional coverage, without additionalunderwriting or analysis. For example, in some embodiments, once basecoverage has been determined for a business, the underlying ratingvariables have been created. To price an optional coverage, an agent maysimply interact with an agent terminal to quickly obtain pricing for theoptional coverage.

Pricing the optional coverage continues at 504 where the basic premium(or the net premium, where the net premium is the net premium of thepredominant location on the policy) associated with the property isobtained (from the original underwriting analysis). Next, at 506 the netrate is computed by first identifying the value of the property (e.g.,from the information entered for the base policy application). The valueof the property is divided by the premium to arrive at the net rate.Processing continues at 508 where the net rate is multiplied by theamount of the optional endorsement to arrive at a monthly premium amountfor the optional coverage. As an illustrative example, assume an insuredproperty has a value of $8 Million, and the base policy premium is $80k, resulting in a net rate of 0.01. If the building owner wishes toobtain an optional $30 k endorsement, the monthly premium for theendorsement is $30 k*0.01 or $30. The quote for the optional coverage isthen delivered at 510. Applicants have discovered that such an optionalcoverage net rating method ensures that rating variables are “built in”to the optional coverage pricing and allow the efficient, automated, andpredictable pricing of optional coverages.

Pursuant to some embodiments, fees associated with other underwritingexpenses are assigned to individual policies based on industry, risk,and coverage composition factors, allowing expenses to be fairly andpredictably be allocated among policy holders. Applicants recognizedthat a number of “other” underwriting expenses are associated withunderwriting business insurance policies, and that the fair andpredictable allocation of those other underwriting expenses is required.Pursuant to some embodiments, expenses are split to fixed and variable.Applicants have recognized that the fixed fee burden from a given policyvaries with the complexity of the insured based on the likelihood ofincurring claim adjuster time and travel expense, ADM validationrequirements, loss control report order occurrences and call centervolume.

An expense fee calculation process 600 is shown in FIG. 6. Process 600begins at 602 where the base policy is priced (e.g., using the process400 of FIG. 4). At 604 the automated processing platform 110 operates(e.g., using the quoting module 114) to determine the likelihood of theparticular policyholder (or potential policyholder) incurringadministrative expenses. The likelihood may be determined, for example,by consulting expense fee tables stored in rating database 120. Pursuantto some embodiments, the expense fee tables include allocationsdeveloped using both generalized linear model (“GLM”) frameworks topredict the likelihood of expense occurrence and actual expenseinformation. As an example, in some embodiments, the followingallocations are used: (1) pure overhead expenses are allocated as afixed fee per policy, (2) administration validation expenses areallocated based on a calculated likelihood of validation, (3) losscontrol survey expenses are allocated based on a calculated likelihoodof a survey being required, (4) administration expenses are allocatedbased on claim handler salary and expense information, and (5) billingexpenses are allocated based on call center volume.

For those allocations (such as items (2)-(3), above) requiring acalculated likelihood, processing continues at 606 and embodimentsutilize actual cost information in conjunction with a predictive model(such as a GLM) to determine the likelihood that a particular policyholder requiring a particular expense. The result is an expense feecalculation that fairly and reliably applies other underwriting expensesacross policies. The expense fee is used to update the quote andreturned to the agent device for delivery to the business owner orrepresentative.

FIG. 7 is a block diagram of an insurance apparatus 700 in accordancewith some embodiments of the present invention. The apparatus 700 might,for example, comprise a platform or engine similar to the platform 110illustrated in FIG. 1. The apparatus 700 comprises a processor 710, suchas one or more INTEL® Pentium® processors, coupled to a communicationdevice 720 configured to communicate via a communication network (notshown in FIG. 7). The communication device 720 may be used to exchangequote requests and quotes, for example, with one or more remote devices(e.g., such as the agent devices 130 a-n of FIG. 1) and to retrieve orreceive data from third party data sources (e.g., such as data sources140 a-n of FIG. 1).

The processor 710 is also in communication with an input device 740. Theinput device 740 may comprise, for example, a keyboard, a mouse, orcomputer media reader. Such an input device 740 may be used, forexample, to enter information about existing or proposed policy ratingmethodologies, rating schedules, or the like. The processor 710 is alsoin communication with an output device 750. The output device 750 maycomprise, for example, a display screen or printer. Such an outputdevice 750 may be used, for example, to provide reports and/or displayinformation associated with policy rating methodologies, ratingschedules, quotes, or the like.

The processor 710 is also in communication with a storage device 730.The storage device 730 may comprise any appropriate information storagedevice, including combinations of magnetic storage devices (e.g., harddisk drives), optical storage devices, and/or semiconductor memorydevices such as Random Access Memory (RAM) devices and Read Only Memory(ROM) devices.

The storage device 730 stores a program 715 for controlling theprocessor 710. The processor 710 performs instructions of the program715, and thereby operates in accordance any embodiments of the presentinvention described herein. For example, the processor 710 may determineapplicable coverages, including professional liability coverage,associated with a quote request received from an agent device. Theprocessor 710 may also identify relevant formula(s) to be used inconstructing and delivering a quote for a new or updated policy. Theprocessor 710 may also calculate and deliver pricing for one or moreoptional coverages and/or expense fees associated with a policy.

As used herein, information may be “received” by or “transmitted” to,for example: (i) the insurance apparatus 700 from other devices; or (ii)a software application or module within the insurance apparatus 700 fromanother software application, module, or any other source.

As shown in FIG. 7, the storage device 730 also stores or is otherwisein communication with a rating data 120 (e.g., such as the database 120of FIG. 1) and/or an insurance policy database 1300. Any number of otherdatabases or database arrangements could be employed besides thosesuggested by the figures. For example, different databases associatedwith different types of rating methodologies, businesses, or policiesmight be associated with the apparatus 700.

The insurance apparatus 700 may be used to offer low to medium hazardclasses of businesses a professional liability insurance expansionopportunity. Note that many small business owners may considerprofessional liability insurance to be a necessary coverage they wouldprefer to be embedded into a typical business owner's insurance policy.In this way, a product feature may provide enhanced coverage tailored tothe needs of small business owners. In particular, a robust base formmight be associated with disciplinary proceedings defense coverage,personal injury coverage, a settlement clause, punitive damagescoverage, a defense within limits clause, a subpoena assistance defensecoverage clause, and/or a mediation resolution give back clause.

Note that different types or classes of businesses might be interestedin professional liability insurance. By way of example only, the classesof businesses might include an accounting and auditing service, anadvertising agency, an answering service, a business and managementconsultant, a wedding consultant, a copying and duplicating service, acourt reporting service, a graphic artist and designer, an embroideryoperation, an interior decorator, an interpreter or translator, a marketresearch firm, a notary public, a public speaker, a stenographic andsecretarial service, a tax preparer and bookkeeper, a telemarketingfirm, and/or) a travel agency.

Note that, with respect to professional liability insurance, differentbusiness types or classes may be associated with different underwritingrisks. Moreover, different underwriting risks might be associated withdifferent insurance processes. For example, FIG. 11 is a high leveloverview of insurance processes 1100 associated with various categoriesof underwriting risks according to some embodiments. In particular, afirst process 1110 associated with a relatively low hazard business type(e.g., embroidery, answering service, etc.). In the first process 1110,class specific questions for a new professional liability submission mayresult in either a bindable quote or a hard stop. In the case of arenewal, an updated application might not be required in the firstprocess 1110.

A second process 1120 may be associated with a mid-level hazard businesstype (e.g., copying service, advertising agency, etc.). In the secondprocess 1120, all new business underwriting is performed by aprofessional liability specialist. In the case of a renewal, an updatedapplication might be required every three years in the second process1120. A third process 1130 may be associated with a relatively highhazard business type (e.g., accountants, business/managementconsultants, etc.). In the third process 1130, all new businessunderwriting is performed by a professional liability specialist. In thecase of a renewal, an updated application might be required every yearin the second process 1130.

FIG. 12 illustrates a GUI display 1200 to provide professional liabilityrating information according to some embodiments. In particular, a firstarea 1210 of the display 1200 may be used to provide location specificrating information, including at least one of a base rate and a businesssize. A second area 1220 of the display 1200 may be used to providepolicy level rating information, including at least one of: (i) a hazardgroup, (ii) a business class, (iii) a layer, (iv) a defense type, (v)prior acts, (vi) prior claims, (vii) years in business, (viii) years ofprofessional experience, (ix) a professional association, and/or (x) afinancial condition.

The final rating structure may be determined by locations values for anumber of different locations associated with the business (threedifferent locations in the example of FIG. 12) and a minimum premiumvalue to establish a final premium value to be used in the quote.

FIG. 13 is a portion of a tabular insurance policy database 1300 storinginsurance information in accordance with some embodiments. The table mayinclude, for example, entries identifying insurance policies and/orinsurance quotes. The table may also define fields 1302, 1304, 1306,1308, 1310 for each of the entries. The fields 1302, 1304, 1306, 1308,1310 may, according to some embodiments, specify: an insurance policyidentifier 1302, a price of business owner's policy coverage 1304,product feature 1306, a price of professional liability coverage 1308,and indications of one or more insurance claims 1310. The insurancepolicy database 1300 may be created and updated, for example, based oninformation electrically received from a potential insured, receivedfrom an insurance agent, and/or that is automatically determined and/orpredicted by an insurance enterprise.

The insurance policy identifier 1302 may be, for example, a uniquealphanumeric code identifying an insurance policy or quote (and may beassociated with a ZIP code, town name, latitude and longitude,geographic state, street address, etc.). The price of business owner'spolicy coverage 1304 might indicate the price determined based on theZIP code. The product feature 1306 may, for example, be mandatory and/orclass-specific—such as a coverage limit or deductible amount. The priceof professional liability coverage 1308 might have been calculated,according to some embodiments, in parallel with the price of businessowner's policy coverage 1304. The indications of one or more insuranceclaims 1310 might be associated with normal business insurance claimsand/or professional liability insurance claims. By having all of theclaim information in a single database, the ability to analyze andaccess the data may be improved.

FIG. 14 illustrates a tablet computer displaying insurance relatedinformation 1400 according to some embodiments. The information 1400includes an insurance policy identifier, a price of business owner'spolicy coverage, endorsement product feature, a price of professionalliability coverage, and indications of one or more insurance claims. Byhaving a single GUI display include both business insurance andprofessional liability insurance information, the ability of an agent orbusiness owner to understand the information may be improved.

As a result of the embodiments described herein, improved rate andpricing specificity and flexibility for business insurance policies maybe achieved for both business owner's insurance and professionalliability insurance. Further, embodiments allow some or all stepsassociated with the quoting process to be automated and/or performed inparallel, thereby reducing errors and improving efficiency of thequoting process. Embodiments establish base rates by coverage, amount ofinsurance relativities, territories and using an enhanced pricing modelbased on historical loss data and current demographic, geographical andother data.

The following illustrates various additional embodiments of theinvention. These do not constitute a definition of all possibleembodiments, and those skilled in the art will understand that thepresent invention is applicable to many other embodiments. Further,although the following embodiments are briefly described for clarity,those skilled in the art will understand how to make any changes, ifnecessary, to the above-described apparatus and methods to accommodatethese and other embodiments and applications.

Although specific hardware and data configurations have been describedherein, not that any number of other configurations may be provided inaccordance with embodiments of the present invention (e.g., some of theinformation associated with the databases described herein may becombined or stored in external systems).

Pursuant to some embodiments, features from the territorial analysisdescribed above may be extended to model and create classificationstructures for catastrophe-related losses (e.g., such as hurricanes orthe like). As an initial step, classification structures for catastropheand non-catastrophe losses were modeled separately (e.g., using GLMtechniques). Then, pursuant to some embodiments, to create a total lossclassification system, the total of the modeled results for thecatastrophe and non-catastrophe losses was further modeled. Applicantsrecognized that such an approach allowed a more granular analysis andthe creation of accurate and relevant results for territories (such asStates or other geographical regions) with catastrophe exposure. In thismanner, catastrophe losses may be included in premiums in proportion tonon-catastrophe premiums

The present invention has been described in terms of several embodimentssolely for the purpose of illustration. Persons skilled in the art willrecognize from this description that the invention is not limited to theembodiments described, but may be practiced with modifications andalterations limited only by the spirit and scope of the appended claims.

What is claimed is:
 1. A system to improve processing associated with anautomated dynamic rating application, comprising: a communication deviceto receive, from a remote device via a communication network, singleinput information provided at the remote device including informationassociated with a business including a quote request for insurancecoverage for the business, and to transmit the single input informationto a plurality of platforms, thereby reducing errors in inputinformation; a first computing platform coupled to the communicationdevice, including: a first processor coupled to the communicationdevice; and a first storage device in communication with the firstprocessor and storing instructions defining an automated dynamic ratingapplication and adapted to be executed by the processor to: receive, viathe communication device, the single input information provided at theremote device; query a rating database using at least a first relevantcoverage formula and the single input information, the query resultingin at least a first price for at least a first applicable insurancecoverage for the business; and a second computing platform coupled tothe communication device, operating in parallel with the first computingplatform, including: a second processor; a second storage device incommunication with the second processor and storing instructions adaptedto be executed by the second processor to: receive, via thecommunication device, the single input information provided at theremote device; determine, using the single input information and atleast one product feature, a professional liability premium value,wherein the second computing platform is configured to process thesingle input information in parallel with the first computing platform,thereby providing an expedited response to the single input information;wherein the system is configured to generate and provide to the remotedevice, in response to the single input information, a single graphicaluser interface display showing results from the first and secondplatforms, including a price of coverage corresponding to the at leastfirst price and the professional liability premium value.
 2. The systemof claim 1, wherein the quote request included with the single inputinformation provided at the remote device further includes dataindicative of a business type and a business location for the business.3. The system of claim 2, wherein the first storage device furtherstores instructions adapted to be executed by the processor to:identify, based upon the single input information, the at least firstapplicable insurance coverage for the business based on receipt of thebusiness type and the business location from the communication deviceresponsive to the request for quote.
 4. The system of claim 3, whereinthe first storage device further stores instructions adapted to beexecuted by the processor to: identify the at least first relevantcoverage formula based on the identified at least first applicableinsurance coverage for the business, the at least first relevantcoverage formula including at least a first territory factor and datalookups to retrieve and apply a relevant rate from a business owner'spolicy coverage rating database, the at least a first territory factorbased on the geographical location of the business.
 5. The system ofclaim 2, wherein the second storage device further stores instructionsadapted to be executed by the processor to: responsive to receipt of thesingle input information provided at the remote device and responsive tothe quote request, based on the business type automatically received inparallel with the first computing platform from the communicationdevice, modify a base professional liability form with at least oneproduct feature, and query a professional liability rating databaseusing the business type, the business location, and the at least oneproduct feature, to determine the professional liability premium value.6. The system of claim 2, wherein the system is further configured totransmit a response to the quote request, the response including the atleast first price, the at least first applicable coverage, and theprofessional liability premium value.
 7. The system of claim 1, whereinthe business owner's policy coverage rating database is generated basedupon outputs from one or more computerized predictive models trained onhistorical loss data.
 8. The system of claim 7, wherein the outputs ofthe predictive models comprise data identifying one or more significantgeo variables which reduce a geographical or a demographic influence onclaim or loss data.
 9. A method for dynamic processing of automatedrating applications, comprising: receiving, by a communication device ofa system from a remote device via a communication network, single inputinformation provided at the remote device including informationassociated with a business to be insured including a quote request forinsurance coverage for the business, and to transmit the single inputinformation to a plurality of platforms, thereby reducing errors ininput information; receiving, via the communication device by a firstcomputing platform coupled to the communication device, the single inputinformation provided at the remote device; querying, by the firstcomputer platform, a rating database using at least a first relevantcoverage formula, the single input information, the query resulting inat least a first price for at least a first applicable insurancecoverage for the business; and receiving, via the communication deviceby a second computing platform coupled to the communication device, thesingle input information provided at the remote device; determining, bythe second computing platform, using the single input information and atleast one product feature, a professional liability premium value,processing, by the second computing platform, the single inputinformation in parallel with the first computing platform, therebyproviding an expedited response to the single input information; andgenerating, by the system, and providing to the remote device by thefirst computing platform and the second computer platform, in responseto the single input information, a single graphical user interfacedisplay showing results from the first and second platforms, including apolicy identifier, a price of coverage corresponding to the at leastfirst price, the at least one product feature, and the professionalliability premium value.
 10. The method of claim 9, wherein the quoterequest included with the single input information provided at theremote device further includes data indicative of a business type and abusiness location for the business.
 11. The method of claim 10, furthercomprising identifying, by the first computer platform, based upon thesingle input information, the at least first applicable insurancecoverage for the business based on receipt of the business type and thebusiness location from the communication device responsive to therequest for quote.
 12. The method of claim 11, further comprisingidentifying, by the first computer platform, based on the identified atleast first applicable insurance coverage for the business, the at leastfirst relevant coverage formula including at least a first territoryfactor and data lookups to retrieve and apply a relevant rate from abusiness owner's policy coverage rating database, the at least a firstterritory factor based on the geographical location of the business. 13.The method of claim 10, further comprising modifying, by the secondcomputer platform, responsive to receipt of the single input informationprovided at the remote device and responsive to the quote request, basedon the business type automatically received in parallel with the firstcomputing platform from the communication device, a base professionalliability form with the at least one product feature, and whereindetermining the professional liability premium value comprises queryinga professional liability rating database using the business type, thebusiness location, and the at least one product feature, the queryresulting in determination of the professional liability premium value.14. The method of claim 10, further comprising transmitting, by thesystem, a response to the quote request, the response including the atleast first price, the at least first applicable coverage, and theprofessional liability premium value.
 15. The method of claim 9, furthercomprising generating by a processor, based upon outputs from one ormore computerized predictive models trained on historical loss data, thebusiness owner's policy coverage rating database, wherein the outputs ofthe predictive models comprises data identifying one or more significantgeo variables which reduce a geographical or a demographic influence onclaim or loss data.
 16. The method of claim 15, wherein generating theinsurance coverage for the business further comprises performing aniterative clustering and cluster reduction process to generate a set ofbest geographic clusters for application of territory factors.